A Comparative Study on Annotation Quality of Crowdsourcing and LLm Via Label Aggregation
Author:
Affiliation:
1. University of Yamanashi,Kofu,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447803.pdf?arnumber=10447803
Reference25 articles.
1. Artificial artificial artificial intelligence: Crowd workers widely use large language models for text production tasks;Veselovsky,2023
2. Can chatgpt reproduce humangenerated labels? a study of social computing tasks;Zhu,2023
3. ChatGPT outperforms crowd workers for text-annotation tasks
4. Chatgpt-4 outperforms experts and crowd workers in annotating political twitter messages with zero-shot learning;Törnberg,2023
5. ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
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1. If in a Crowdsourced Data Annotation Pipeline, a GPT-4;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11
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